@inproceedings{055e7122073249cfb97c1f9f29c2a93d,
title = "Online bad data detection using kernel density estimation",
abstract = "This paper addresses the problem of bad data detection in the power grid. An online probability density based technique is presented to identify bad measurements within a sensor data stream in a decentralized manner using only the data from the neighboring buses and a one-hop communication system. Analyzing the spatial and temporal dependency between the measurements, the proposed algorithm identifies the bad data. The algorithm was then tested on the IEEE 14-bus test system where it demonstrated superior performance detecting critical and multiple bad data compared to the largest normalized residual test.",
keywords = "bad data detection, density estimation, online algorithm, smart grids",
author = "Uddin, {Muhammad Sharif} and Anthony Kuh and Yang Weng and Marija Ili{\'c}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Power and Energy Society General Meeting, PESGM 2015 ; Conference date: 26-07-2015 Through 30-07-2015",
year = "2015",
month = sep,
day = "30",
doi = "10.1109/PESGM.2015.7286013",
language = "English (US)",
series = "IEEE Power and Energy Society General Meeting",
publisher = "IEEE Computer Society",
booktitle = "2015 IEEE Power and Energy Society General Meeting, PESGM 2015",
}